OpenRaiser/Intern-Atlas huggingface dataset:
Plasticity Loss in Deep Reinforcement Learning: A Survey
Venue: arXiv cs.AI 2024
Year: 2024

Akin to neuroplasticity in human brains, the plasticity of deep neural networks enables their quick adaption to new data. This makes plasticity particularly crucial for deep Reinforcement Learning (RL) agents: Once plasticity is lost, an agent's performance will inevitably plateau because it cannot improve its policy to account for changes in the data distribution, which are a necessary consequence of its learning process. Thus, developing well-performing and sample-efficient agents hinges on their ability to remain plastic during training. Furthermore, the loss of plasticity can be connected to many other issues plaguing deep RL, such as training instabilities, scaling failures, overestimation bias, and insufficient exploration. With this survey, we aim to provide an overview of the emerging research on plasticity loss for academics and practitioners of deep reinforcement learning. First, we propose a unified definition of plasticity loss based on recent works, relate it to definitions from the literature, and discuss metrics for measuring plasticity loss. Then, we categorize and discuss numerous possible causes of plasticity loss before reviewing currently employed mitigation strategies. Our taxonomy is the first systematic overview of the current state of the field. Lastly, we discuss prevalent issues within the literature, such as a necessity for broader evaluation, and provide recommendations for future research, like gaining a better understanding of an agent's neural activity and behavior.
ZAHA: Introducing the Level of Facade Generalization and the Large-Scale Point Cloud Facade Semantic Segmentation Benchmark Dataset
Venue: arXiv cs.CV 2024
Year: 2024

Facade semantic segmentation is a long-standing challenge in photogrammetry and computer vision. Although the last decades have witnessed the influx of facade segmentation methods, there is a lack of comprehensive facade classes and data covering the architectural variability. In ZAHA, we introduce Level of Facade Generalization (LoFG), novel hierarchical facade classes designed based on international urban modeling standards, ensuring compatibility with real-world challenging classes and uniform methods' comparison. Realizing the LoFG, we present to date the largest semantic 3D facade segmentation dataset, providing 601 million annotated points at five and 15 classes of LoFG2 and LoFG3, respectively. Moreover, we analyze the performance of baseline semantic segmentation methods on our introduced LoFG classes and data, complementing it with a discussion on the unresolved challenges for facade segmentation. We firmly believe that ZAHA shall facilitate further development of 3D facade semantic segmentation methods, enabling robust segmentation indispensable in creating urban digital twins.
In the Era of Prompt Learning with Vision-Language Models
Venue: arXiv cs.CV 2024
Year: 2024

Large-scale foundation models like CLIP have shown strong zero-shot generalization but struggle with domain shifts, limiting their adaptability. In our work, we introduce \textsc{StyLIP}, a novel domain-agnostic prompt learning strategy for Domain Generalization (DG). StyLIP disentangles visual style and content in CLIP`s vision encoder by using style projectors to learn domain-specific prompt tokens and combining them with content features. Trained contrastively, this approach enables seamless adaptation across domains, outperforming state-of-the-art methods on multiple DG benchmarks. Additionally, we propose AD-CLIP for unsupervised domain adaptation (DA), leveraging CLIP`s frozen vision backbone to learn domain-invariant prompts through image style and content features. By aligning domains in embedding space with entropy minimization, AD-CLIP effectively handles domain shifts, even when only target domain samples are available. Lastly, we outline future work on class discovery using prompt learning for semantic segmentation in remote sensing, focusing on identifying novel or rare classes in unstructured environments. This paves the way for more adaptive and generalizable models in complex, real-world scenarios.
A Reinforcement Learning-Based Automatic Video Editing Method Using Pre-trained Vision-Language Model
Venue: arXiv cs.CV 2024
Year: 2024

In this era of videos, automatic video editing techniques attract more and more attention from industry and academia since they can reduce workloads and lower the requirements for human editors. Existing automatic editing systems are mainly scene- or event-specific, e.g., soccer game broadcasting, yet the automatic systems for general editing, e.g., movie or vlog editing which covers various scenes and events, were rarely studied before, and converting the event-driven editing method to a general scene is nontrivial. In this paper, we propose a two-stage scheme for general editing. Firstly, unlike previous works that extract scene-specific features, we leverage the pre-trained Vision-Language Model (VLM) to extract the editing-relevant representations as editing context. Moreover, to close the gap between the professional-looking videos and the automatic productions generated with simple guidelines, we propose a Reinforcement Learning (RL)-based editing framework to formulate the editing problem and train the virtual editor to make better sequential editing decisions. Finally, we evaluate the proposed method on a more general editing task with a real movie dataset. Experimental results demonstrate the effectiveness and benefits of the proposed context representation and the learning ability of our RL-based editing framework.
Uncovering Hidden Subspaces in Video Diffusion Models Using Re-Identification
Venue: arXiv cs.CV 2024
Year: 2024

Latent Video Diffusion Models can easily deceive casual observers and domain experts alike thanks to the produced image quality and temporal consistency. Beyond entertainment, this creates opportunities around safe data sharing of fully synthetic datasets, which are crucial in healthcare, as well as other domains relying on sensitive personal information. However, privacy concerns with this approach have not fully been addressed yet, and models trained on synthetic data for specific downstream tasks still perform worse than those trained on real data. This discrepancy may be partly due to the sampling space being a subspace of the training videos, effectively reducing the training data size for downstream models. Additionally, the reduced temporal consistency when generating long videos could be a contributing factor. In this paper, we first show that training privacy-preserving models in latent space is computationally more efficient and generalize better. Furthermore, to investigate downstream degradation factors, we propose to use a re-identification model, previously employed as a privacy preservation filter. We demonstrate that it is sufficient to train this model on the latent space of the video generator. Subsequently, we use these models to evaluate the subspace covered by synthetic video datasets and thus introduce a new way to measure the faithfulness of generative machine learning models. We focus on a specific application in healthcare echocardiography to illustrate the effectiveness of our novel methods. Our findings indicate that only up to 30.8% of the training videos are learned in latent video diffusion models, which could explain the lack of performance when training downstream tasks on synthetic data.
Planar Reflection-Aware Neural Radiance Fields
Venue: arXiv cs.CV 2024
Year: 2024

Neural Radiance Fields (NeRF) have demonstrated exceptional capabilities in reconstructing complex scenes with high fidelity. However, NeRF's view dependency can only handle low-frequency reflections. It falls short when handling complex planar reflections, often interpreting them as erroneous scene geometries and leading to duplicated and inaccurate scene representations. To address this challenge, we introduce a reflection-aware NeRF that jointly models planar reflectors, such as windows, and explicitly casts reflected rays to capture the source of the high-frequency reflections. We query a single radiance field to render the primary color and the source of the reflection. We propose a sparse edge regularization to help utilize the true sources of reflections for rendering planar reflections rather than creating a duplicate along the primary ray at the same depth. As a result, we obtain accurate scene geometry. Rendering along the primary ray results in a clean, reflection-free view, while explicitly rendering along the reflected ray allows us to reconstruct highly detailed reflections. Our extensive quantitative and qualitative evaluations of real-world datasets demonstrate our method's enhanced performance in accurately handling reflections.
Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, most existing work treats diffusion models as a standalone component for perception tasks, employing them either solely for off-the-shelf data augmentation or as mere feature extractors. In contrast to these isolated and thus sub-optimal efforts, we introduce a unified, versatile, diffusion-based framework, Diff-2-in-1, that can simultaneously handle both multi-modal data generation and dense visual perception, through a unique exploitation of the diffusion-denoising process. Within this framework, we further enhance discriminative visual perception via multi-modal generation, by utilizing the denoising network to create multi-modal data that mirror the distribution of the original training set. Importantly, Diff-2-in-1 optimizes the utilization of the created diverse and faithful data by leveraging a novel self-improving learning mechanism. Comprehensive experimental evaluations validate the effectiveness of our framework, showcasing consistent performance improvements across various discriminative backbones and high-quality multi-modal data generation characterized by both realism and usefulness.
Cast vote records: A database of ballots from the 2020 U.S. Election
Venue: arXiv cs.CY 2024
Year: 2024

Ballots are the core records of elections. Electronic records of actual ballots cast (cast vote records) are available to the public in some jurisdictions. However, they have been released in a variety of formats and have not been independently evaluated. Here we introduce a database of cast vote records from the 2020 U.S. general election. We downloaded publicly available unstandardized cast vote records, standardized them into a multi-state database, and extensively compared their totals to certified election results. Our release includes vote records for President, Governor, U.S. Senate and House, and state upper and lower chambers -- covering 42.7 million voters in 20 states who voted for more than 2,204 candidates. This database serves as a uniquely granular administrative dataset for studying voting behavior and election administration. Using this data, we show that in battleground states, 1.9 percent of solid Republicans (as defined by their congressional and state legislative voting) in our database split their ticket for Joe Biden, while 1.2 percent of solid Democrats split their ticket for Donald Trump.
Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey
Venue: arXiv cs.CV 2024
Year: 2024

SAR images possess unique attributes that present challenges for both human observers and vision AI models to interpret, owing to their electromagnetic characteristics. The interpretation of SAR images encounters various hurdles, with one of the primary obstacles being the data itself, which includes issues related to both the quantity and quality of the data. The challenges can be addressed using generative AI technologies. Generative AI, often known as GenAI, is a very advanced and powerful technology in the field of artificial intelligence that has gained significant attention. The advancement has created possibilities for the creation of texts, photorealistic pictures, videos, and material in various modalities. This paper aims to comprehensively investigate the intersection of GenAI and SAR. First, we illustrate the common data generation-based applications in SAR field and compare them with computer vision tasks, analyzing the similarity, difference, and general challenges of them. Then, an overview of the latest GenAI models is systematically reviewed, including various basic models and their variations targeting the general challenges. Additionally, the corresponding applications in SAR domain are also included. Specifically, we propose to summarize the physical model based simulation approaches for SAR, and analyze the hybrid modeling methods that combine the GenAI and interpretable models. The evaluation methods that have been or could be applied to SAR, are also explored. Finally, the potential challenges and future prospects are discussed. To our best knowledge, this survey is the first exhaustive examination of the interdiscipline of SAR and GenAI, encompassing a wide range of topics, including deep neural networks, physical models, computer vision, and SAR images. The resources of this survey are open-source at \url{https://github.com/XAI4SAR/GenAIxSAR}.
Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal Language Models
Venue: arXiv cs.CR 2024
Year: 2024

Multi-Modal Language Models (MLLMs) have transformed artificial intelligence by combining visual and text data, making applications like image captioning, visual question answering, and multi-modal content creation possible. This ability to understand and work with complex information has made MLLMs useful in areas such as healthcare, autonomous systems, and digital content. However, integrating multiple types of data also creates security risks. Attackers can manipulate either the visual or text inputs, or both, to make the model produce unintended or even harmful responses. This paper reviews how visual inputs in MLLMs can be exploited by various attack strategies. We break down these attacks into categories: simple visual tweaks and cross-modal manipulations, as well as advanced strategies like VLATTACK, HADES, and Collaborative Multimodal Adversarial Attack (Co-Attack). These attacks can mislead even the most robust models while looking nearly identical to the original visuals, making them hard to detect. We also discuss the broader security risks, including threats to privacy and safety in important applications. To counter these risks, we review current defense methods like the SmoothVLM framework, pixel-wise randomization, and MirrorCheck, looking at their strengths and limitations. We also discuss new methods to make MLLMs more secure, including adaptive defenses, better evaluation tools, and security approaches that protect both visual and text data. By bringing together recent developments and identifying key areas for improvement, this review aims to support the creation of more secure and reliable multi-modal AI systems for real-world use.
Presenting the Sense of Effort through Vibration Based on Force Estimated by Inverse Dynamics in Videos
Venue: arXiv cs.HC 2024
Year: 2024

We present the sense of effort through vibration to help the video viewer understand how the person in the video moves the body. We suppose sense of effort is related to force, so we generate vibration based on force and present the sense of effort through the vibration. We use perceived intensity to make sense of effort proportional to vibration. In our demonstration, you can experience vibration while watching a video. We can create vibration on the spot, so you can experience vibration made from a video taken on the spot.
Emotional VR handshake by controlling skin deformation distribution
Venue: arXiv cs.HC 2024
Year: 2024

Digital communication tools are limited to visual and auditory information and lack non-verbal information such as touch, which is important for communicating intentions and emotions. In order to solve this problem, the use of haptic technology in digital communication is attracting attention. In this study, we constructed a virtual handshake system that can reproduce distributed haptic information using a wearable device that presents skin deformation. Using the system, we experimentally obtained the correspondence between emotions and handshaking behavior, and constructed a demonstration of handshakes that can express differences in emotions based on the experimental results.
Silicone-made Tactile Actuator Integrated with Hot Thermo-fiber Finger Sleeve
Venue: arXiv cs.HC 2024
Year: 2024

Multi-mode haptic feedback is essential to achieve high realism and immersion in virtual environments. This paper proposed a novel silicone fingertip actuator integrated with a hot thermal fabric finger sleeve to render pressure, vibration, and hot thermal feedback simultaneously. The actuator is pneumatically actuated to render a realistic and effective tactile experience in accordance with hot thermal sensation. The silicone actuator, with two air chambers controlled by pneumatic valves connected to compressed air tanks. Simultaneously, a PWM signal from a microcontroller regulates the temperature of the thermal fabric sleeve, enhancing overall system functionality. The lower chamber of the silicone actuator is responsible for pressure feedback, whereas the upper chamber is devoted to vibrotactile feedback. The conductive yarn or thread was utilized to spread the thermal feedback actuation points on the thermal fabric's surface. To demonstrate the actuator's capability, a VR environment consisting of a bowl of liquid and a stove with fire was designed. Based on different functionalities the scenario can simulate the tactile perception of pressure, vibration, and temperature simultaneously or consecutively.
DynaPain: Moving Flame Beetle with Dynamic Pain Illusion Adapting Apparent Movement to Thermal Grill Illusion
Venue: arXiv cs.HC 2024
Year: 2024

Pain sensation presentation with movable sensory position is important to imitate the pain caused by objects in motion and the pain corresponding to a person's movements. We aimed at proposing a novel dynamic pain sensation experience, called DynaPain. DynaPain was achieved by the non-contact thermal grill illusion and the apparent movement. The demonstration provided the dynamic heat and pain experience through interaction with a flame beetle moving on the arm.
visionFinGAR: Transmission of Softness and Shape Motion by Vision Based Tactile Sensor and Combination of Mechanical and Electrical Stimulation
Venue: arXiv cs.HC 2024
Year: 2024

This paper describes a system for transmitting softness and the motion of shape or contact area sensation using a vision based tactile sensor and a tactile display in which mechanical and electrical stimulation are combined. A unit of tactile sensor consists of a camera and markers, enable to detect a light touch, a pressure or a shape. On the other hand, a unit of tactile display consists of an electrode array and a mechanical arm to provide softness / pressure and shape perception. The display can provide four mode stimulation: anodic, cathodic, mechanical vibration and skin deformation; thus, it can reproduce a large range of tactile sensations. This study mainly aims to transmit a wide range of softness and shape motion perception with a vision based tactile sensor.
Soft or Stiff? Stroop Tasks in Visuo-Tactile Tapping Interactions
Venue: arXiv cs.HC 2024
Year: 2024

One of the key challenges in the field of haptic research is designing plausible stimuli using haptic interfaces with limited degrees of freedom. Although the plausible approach, which simplifies and/or exaggerates stimuli to enhance information transfer or create an artistic effect, has proven effective, evaluations of such stimuli have traditionally relied on subjective measures. This study aims to establish an objective evaluation method for haptic stimuli designed using the plausible approach. Focusing on stiffness/material perception, we developed a Stroop test within visuo-tactile tapping interactions in a virtual space. The demonstration system presents visual (textures) and tactile (vibration) stimuli at the moment of contact between a stylus and a cube, prompting participants to immediately identify the material they perceive visually. If the tactile stimuli are perceived as plausible, reaction times will be longer when the visual and tactile stimuli represent different materials than when they represent the same material.
Out-of-body Localization of Virtual Vibration Sources Using a Limited Numbers of Transducers on the Torso
Venue: arXiv cs.HC 2024
Year: 2024

Stereohaptic vibration is an innovative vibrotactile technology that extends the conventional tactile localization to the surrounding space, representing a virtual vibration source in the external environment. Previously, we have developed displays on the forearms and soles. Today, we present a demonstration of a new jacket-type device, which enables localization at any position around the body by arranging multiple vibrators along the torso centered on the midline. In our demonstration, you can experience the footsteps and roars of a inosaur walking around you, and it provides an experience that is as if you are in a fantasy movie.
EPIC: Enhancing Privacy through Iterative Collaboration
Venue: arXiv cs.LG 2024
Year: 2024

Advancements in genomics technology lead to a rising volume of viral (e.g., SARS-CoV-2) sequence data, resulting in increased usage of machine learning (ML) in bioinformatics. Traditional ML techniques require centralized data collection and processing, posing challenges in realistic healthcare scenarios. Additionally, privacy, ownership, and stringent regulation issues exist when pooling medical data into centralized storage to train a powerful deep learning (DL) model. The Federated learning (FL) approach overcomes such issues by setting up a central aggregator server and a shared global model. It also facilitates data privacy by extracting knowledge while keeping the actual data private. This work proposes a cutting-edge Privacy enhancement through Iterative Collaboration (EPIC) architecture. The network is divided and distributed between local and centralized servers. We demonstrate the EPIC approach to resolve a supervised classification problem to estimate SARS-CoV-2 genomic sequence data lineage without explicitly transferring raw sequence data. We aim to create a universal decentralized optimization framework that allows various data holders to work together and converge to a single predictive model. The findings demonstrate that privacy-preserving strategies can be successfully used with aggregation approaches without materially altering the degree of learning convergence. Finally, we highlight a few potential issues and prospects for study in FL-based approaches to healthcare applications.
Inverse Transition Learning: Learning Dynamics from Demonstrations
Venue: arXiv cs.LG 2024
Year: 2024

We consider the problem of estimating the transition dynamics $T^*$ from near-optimal expert trajectories in the context of offline model-based reinforcement learning. We develop a novel constraint-based method, Inverse Transition Learning, that treats the limited coverage of the expert trajectories as a \emph{feature}: we use the fact that the expert is near-optimal to inform our estimate of $T^*$. We integrate our constraints into a Bayesian approach. Across both synthetic environments and real healthcare scenarios like Intensive Care Unit (ICU) patient management in hypotension, we demonstrate not only significant improvements in decision-making, but that our posterior can inform when transfer will be successful.